Inferring adaptation from population genomics data: opportunities and challenges

2.00-2.20 pm

Prof. Frédéric Guillaume, University of Zurich, Switzerland

Genomics and the forecasting of species' responses to climate change

A large part of ecologically relevant traits are quantitative traits. As such, their genetic basis is likely polygenic, and their variation due to many loci each contributing a small portion to that variation. The eco-evolutionary dynamics of populations responding to changes in their environment depend, in part, on the genetic architecture of the traits under selection. In this talk, I will review how the evolutionary dynamics of quantitative traits depend on their genetic architecture, emphasizing the consequences of their polygenic basis. I will then show how we can build integrated eco-evolutionary modeling approaches to predict species' responses to climate change. I will illustrate this approach with a recently published eco-evolutionary model of the response to climate change of endemic alpine plant species and the Austrian Alps. This work compares prediction of shifts of species' ranges using classical species distribution models (SDMs) with our new eco-evolutionary forecasting framework. We show that, by taking precise account of the demographic and evolutionary dynamics of the perennial plants modeled, we predict a rapid demographic decline caused by a delayed evolutionary answer of local populations. Overall, the SDM predictions show a faster decline of the species' ranges compared to the eco-evo approach, in part because of the positive effect of migration in the later. In closing, I will emphasize how genomic data can be used to enhance our ability to model and predict species' eco-evolutionary dynamics in changing environments.

Exploring the power of ‘genomic’ quantitative genetics for understanding and predicting adaptation

Phillip Gienapp, Frédéric Guillaume, Katalin Csilléry

Polygenic adaptation to environmental can be well understood and, to some extent, predicted by the traits’ genetic (co)variances. Quantitative genetic analyses require information about relatedness among individuals to estimate these genetic (co)variances. This information can be obtained from breeding experiments or pedigrees, which, however, limits QG to certain taxa. Relatedness can also be estimated from molecular markers but early applications suffered from the limited numbers of available markers. Advancements in molecular genetic technology allow now cost-effective genotyping for hundreds to thousands of markers, in virtually any species. ‘Genomic’ quantitative genetics, based on relatedness estimates from high-density markers, is hence possible in theoretically any species and is also likely to give more accurate quantitative genetic estimates. Furthermore, it allows quantifying individual (inclusive) fitness by calculating an individuals’ average relatedness to the population as fitness measure. We here demonstrate the applicability and usefulness of this approach using simulated and empirical data from a range of species. Consequently, ‘genomic’ quantitative genetics has the potential to substantially advance and widen our understanding of adaptation in natural populations.